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Revision 72128874

Added by Benoit Parmentier about 10 years ago

scaling up NEX assessement part2, Asia adding number of daily predictions

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climate/research/oregon/interpolation/global_run_scalingup_assessment_part2.R
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#Analyses, figures, tables and data are also produced in the script.
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#AUTHOR: Benoit Parmentier
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#CREATED ON: 03/23/2014
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#MODIFIED ON: 10/05/2014
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#MODIFIED ON: 10/21/2014
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#Version: 3
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#PROJECT: Environmental Layers project
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#COMMENTS: analyses for run 5 global using 6 specific tiles
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#on ATLAS
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#in_dir1 <- "/data/project/layers/commons/NEX_data/test_run1_03232014/output" #On Atlas
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#parent output dir : contains subset of the data produced on NEX
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in_dir1 <- "/data/project/layers/commons/NEX_data/output_run6_global_analyses_09162014/output20Deg2"
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#in_dir1 <- "/data/project/layers/commons/NEX_data/output_run6_global_analyses_09162014/output20Deg2"
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# parent output dir for the curent script analyes
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#out_dir <-"/data/project/layers/commons/NEX_data/output_run3_global_analyses_06192014/" #On NCEAS Atlas
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out_dir <-"/data/project/layers/commons/NEX_data/output_run7_global_analyses_10042014/"
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out_dir <-"/data/project/layers/commons/NEX_data/output_run8_global_analyses_10212014/"
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# input dir containing shapefiles defining tiles
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#in_dir_shp <- "/data/project/layers/commons/NEX_data/output_run5_global_analyses_08252014/output/subset/shapefiles"
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  ......




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y_var_name <- "dailyTmax"


  		




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interpolation_method <- c("gam_CAI")


  		




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out_prefix<-"run7_global_analyses_10042014"


  		




  
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out_prefix<-"run8_global_analyses_10212014"


  		




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mosaic_plot <- FALSE


  		




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proj_str<- CRS_WGS84


  		




  ......




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                                           pred_mod!="mod_kr"),type="h")


  		




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dev.off()


  		




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table(tb$pred_mod)


  		




  
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table(tb$index_d)


  		




  
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table(subset(tb,pred_mod!="mod_kr"))


  		




  
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table(subset(tb,pred_mod=="mod1")$index_d)


  		




  
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aggregate()


  		




  
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tb$predicted <- 1


  		




  
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test <- aggregate(predicted~pred_mod+tile_id,data=tb,sum)


  		




  
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xyplot(predicted~pred_mod | tile_id,data=subset(as.data.frame(test),


  		




  
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                                           pred_mod!="mod_kr"),type="h")


  		




  
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test


  		




  
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LST_avgm_min <- aggregate(LST~month,data=data_month_all,min)


  		




  
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histogram(test$predicted~test$tile_id)


  		




  
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table(tb)


  		




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## Figure 7b


  		




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#png(filename=paste("Figure7b_number_daily_predictions_per_models","_",out_prefix,".png",sep=""),


  		




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#    width=col_mfrow*res_pix,height=row_mfrow*res_pix)


  		












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